{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "# dataset Iris\n",
    "from sklearn import datasets\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ScatterPlot(X , Y, assignments=None, centers=None):\n",
    "  if assignments is None:\n",
    "    assignments = [0] * len(X)\n",
    "  fig = plt.figure(figsize=(14,8))\n",
    "  cmap = ListedColormap(['red', 'green', 'blue'])\n",
    "  plt.scatter(X, Y, c=assignments, cmap=cmap)\n",
    "  if centers is not None:\n",
    "    plt.scatter(centers[:, 0], centers[:, 1], c=range(len(centers)), \n",
    "                marker='+', s=400, cmap=cmap)  \n",
    "  plt.xlabel('Sepia Length')\n",
    "  plt.ylabel('Sepia Width')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import some data to play with\n",
    "iris = datasets.load_iris()\n",
    "x = iris.data[:, :2]  # we only take the first two features.\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x25c4db50b70>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XRMSr+rxPwC4R8ZSkBcBNwPsj4tY56xwP/CGtpHAYcGFEHNZruzvyTaHz/uvQOh879+t3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25c4db2f208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# original data without clustering\n",
    "plt.scatter(x[:,0], x[:,1])\n",
    "plt.xlabel('Sepia Length')\n",
    "plt.ylabel('Sepia Width')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def input_fn():\n",
    "    return tf.constant(np.array(x), tf.float32, x.shape),None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeansClustering(params={'mini_batch_steps_per_iteration': 1, 'training_initial_clusters': 'random', 'relative_tolerance': 0.0001, 'distance_metric': 'squared_euclidean', 'random_seed': 2, 'kmeans_plus_plus_num_retries': 2, 'use_mini_batch': True, 'num_clusters': 3})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
    "kmeans = tf.contrib.learn.KMeansClustering(num_clusters=3, relative_tolerance=0.0001, random_seed=2)\n",
    "kmeans.fit(input_fn=input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clusters = kmeans.clusters()\n",
    "assignments = list(kmeans.predict_cluster_idx(input_fn=input_fn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NEQAAAICaxNIQ7fX9L2PM3ySdJekrSTtOE7WSWCsHAAAAQKNWmwMCTpHUw1pb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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25c4ff02e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ScatterPlot(x[:,0], x[:,1], assignments, clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1514.6083"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSE = kmeans.score(input_fn=input_fn, steps=100)\n",
    "SSE*SSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x25dbedddc88>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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khbKtbN9fS252BhedHAmSi0/p3e4f/6vwUHiIyHHUNzhvBUEyr6yK6n215GRF\nguTyor5MHNWHvHYYJAoPhYeItFB9g7PkvV1BkFSydW8tHbIyuGBkAVOLCpk4qjddcrPDLjMpFB4K\nDxH5GBoanCXvB0FSWkXV3kN0yMzg/JH5TCkq5JLRfeiaxkGi8FB4iMgJamhw3tm0i7mlVcwrrWTL\nnkNkZxrnjShgSlEhl47uQ7eO6RUkCg+Fh4i0ooYGZ+nm3cwrrWRuaRUVuw+SnWmcMzzSI7lsdJ+0\neF6JwkPhISIJ4u68u3kP80oreb60ks27DpKVYZw9PJ+pRX25bHRfenRum0GS0uFhZhuBfUA9UOfu\nxWbWE/gDMBjYCHzW3XcFy98KfClY/kZ3nx9rGwoPEUkGd6e0Yg9zS6uYW1rJ+zsPkJlhnD2sF1OK\nCpk0pi8921CQtIXwKHb37VFtPwJ2uvsPzOxbQA93/6aZjQaeBMYD/YCXgZHuXt/MRx+l8BCRZHN3\nyrfsZW5pJXNLK9m4IxIkE4b2PBok+Xk5YZd5XG0xPFYBF7p7pZkVAq+6+8lBrwN3/36w3Hxgprv/\n/XjbUHiISJjcneWVe5kX9EjWb68hw+CTQ3ox5dRCJo/pS0GX1AuSVA+PDcAeIoehfuXuD5nZbnfv\nHsw3YJe7dzezXwAL3f13wbxHgHnu/sfjbUPhISKpwt1ZWbXv6DmSddU1mMH4wT2ZGgRJ7665YZcJ\npP7zPM519woz6w28ZGYro2e6u5tZ3KlmZtcB1wEMGjSodSoVETlBZsaowq6MKuzK1y8dyeqt+48e\n2rpzdjl3zSnnEyf1ZEpRXy4vKqRPigTJ8YR+tZWZzQT2A9eiw1Yi0s6s2bqP54MbEldt3YcZnDmo\nB1OKCrm8qC+F3TomtZ6UPWxlZp2BDHffF0y/BNwNTAR2RJ0w7+nu/2lmY4An+OCE+QJghE6Yi0i6\nWbvtgx7Jyqp9AJwxqDtTigqZUlRIv+6JD5JUDo+hwKzgbRbwhLvfa2a9gKeBQcB7RC7V3Rmsczvw\nRaAOuNnd58XajsJDRNqy9dWNQVLF8sq9AIwb2J2pQY9kQI9OCdluyoZHsig8RCRdbNhec3TQxrKK\nSJCcNqDb0R7JwJ6tFyQKD4WHiKSh93bURMbaKqtk2eY9ABT1jwTJ1KJCBvU6sSBReCg8RCTNbdp5\nIHJoq6yKdzftBmBMv6489sXxH/tmxFS/VFdERE7QwJ6d+PIFw/jyBcPYvOsAL5RV8dbGnfRKwnAo\n6nmIiMgLdR7UAAAINklEQVRRLe15ZCSjGBERSS8KDxERiZvCQ0RE4qbwEBGRuCk8REQkbgoPERGJ\nm8JDRETipvAQEZG4pe1NgmZWTWR03o8jH9gec6nkU13xUV3xUV3xSde6TnL3glgLpW14nAgzK2nJ\nHZbJprrio7rio7ri097r0mErERGJm8JDRETipvBo3kNhF/ARVFd8VFd8VFd82nVdOuchIiJxU89D\nRETi1m7Dw8wGmtkrZrbczMrN7KZmljEze8DM1prZMjM7I0XqutDM9pjZ0uB1ZxLqyjWzxWb2blDX\nd5pZJoz91ZK6kr6/oradaWbvmNlzzcxL+v5qYV2h7C8z22hmpcE2P/QwnrD2VwvqCmt/dTezP5rZ\nSjNbYWZnNZmf2P3l7u3yBRQCZwTTXYDVwOgmy0wB5gEGTAAWpUhdFwLPJXl/GZAXTGcDi4AJKbC/\nWlJX0vdX1Lb/HXiiue2Hsb9aWFco+wvYCOQfZ34o+6sFdYW1vx4D/l8w3QHonsz91W57Hu5e6e5v\nB9P7gBVA/yaLTQMe94iFQHczK0yBupIu2Af7g7fZwavpCbMw9ldL6gqFmQ0ApgIPf8QiSd9fLawr\nVYWyv1KRmXUDzgceAXD3w+6+u8liCd1f7TY8opnZYOB0In+1RusPbIp6v5kk/iI/Tl0AZwdd0Xlm\nNiZJ9WSa2VJgG/CSu6fE/mpBXRDC/gLuB/4TaPiI+WH9fMWqC8LZXw68bGZLzOy6ZuaHtb9i1QXJ\n319DgGrgf4PDjw+bWecmyyR0f7X78DCzPOAZ4GZ33xt2PY1i1PU2MMjdTwV+DjybjJrcvd7dxwED\ngPFmNjYZ242lBXUlfX+Z2RXANndfkuhtxaOFdYXy8wWcG/w7Xg5cb2bnJ2m7scSqK4z9lQWcATzo\n7qcDNcC3krDdo9p1eJhZNpFf0L939z81s0gFMDDq/YCgLdS63H1v46Ead58LZJtZfqLritr+buAV\nYHKTWaHsr1h1hbS/zgE+ZWYbgaeAi83sd02WCWN/xawrrJ8vd68Ivm4DZgHjmywSys9XrLpC2l+b\ngc1Rvew/EgmTaAndX+02PMzMiBwvXOHuP/mIxeYAVwdXLUwA9rh7Zdh1mVnfYDnMbDyRf8cdCa6r\nwMy6B9MdgUuBlU0WC2N/xawrjP3l7re6+wB3HwxcBfzF3f+5yWJJ318tqSukn6/OZtalcRq4DChr\nslgYP18x6wrp56sK2GRmJwdNE4HlTRZL6P7Kaq0PaoPOAb4AlAbHywFuAwYBuPv/AHOJXLGwFjgA\n/GuK1PUZ4KtmVgccBK7y4PKKBCoEHjOzTCL/OZ529+fM7CtRdYWxv1pSVxj7q1kpsL9aUlcY+6sP\nMCv4HZwFPOHuL6TA/mpJXWH9fN0A/N7MOgDrgX9N5v7SHeYiIhK3dnvYSkREPj6Fh4iIxE3hISIi\ncVN4iIhI3BQeIiISN4WHtElm5mZ2X9T7b5jZzFb67EfN7DOt8VkxtvMPFhkN9ZVm5o00s7lmtsbM\n3jazp82sj0VGcP3QSLgt3N7NZtbpxCsXUXhI21ULfDqZd9a3hJnFc+/Ul4Br3f2iJp+RCzxPZOiJ\nEe5+BvBLoOAEy7sZiCs8gvtnRD5E4SFtVR2Rx21+vemMpj0HM9sffL3QzF4zs9lmtt7MfmBmn7fI\n80BKzWxY1MdcYmYlZrbaIuNBNQ7A+GMze8sig+B9Oepz/2pmc/jwXb6Y2eeCzy8zsx8GbXcC5wKP\nmNmPm6zyT8Df3f3PjQ3u/qq7N72zeaaZfSPqfZmZDQ7uin7eIs84KTOzfzSzG4F+wCuNPR0zu8zM\n/h70bP7PIuOpNT6/4odm9jbwD2Z2o0WeL7PMzJ6K8e8i7UR7vsNc2r7/BpaZ2Y/iWOc0YBSwk8hd\nuQ+7+3iLPHTrBiJ/nQMMJjKG0TAiv3CHA1cTGeLhE2aWA/zNzF4Mlj8DGOvuG6I3Zmb9gB8CZwK7\ngBfNbLq7321mFwPfcPemDxgaC5zIgIqTgS3uPjWooZu77zGzfwcucvftQY/t28Al7l5jZt8k8oyP\nu4PP2BH0eDCzLcAQd6+1YCgYEfU8pM0KRht+HLgxjtXe8sgzU2qBdUDjL/9SIoHR6Gl3b3D3NURC\n5hQi4xpdHQwbswjoBYwIll/cNDgCnwBedfdqd68Dfk/kOQyJVApcGvQeznP3Pc0sMwEYTSQAlwLX\nACdFzf9D1PQyIsNg/DORHp+IwkPavPuJnDuIfpZBHcHPtpllEHnKWqPaqOmGqPcNHNsTbzpujxN5\nItsN7j4ueA1x98bwqTmh7+JY5UR6KrEc/T4DuQDuvppIT6gUuMeafyyqEXn2SeP3MtrdvxQ1P/r7\nmUqkl3cG8Fac53UkTSk8pE1z953A00QCpNFGPvjl+ykiTxeM1z+YWUZwHmQosAqYT2QAvGw4ekVU\n0wfwNLUYuMDM8oOTz58DXouxzhNEHi40tbHBzM63Dz+nZCPBMNwWeT71kGC6H3DA3X8H/JgPhure\nR+TRxgALgXOCw3GNo8eObFpIEL4D3f0V4JtANyAvRv3SDugvCEkH9wH/FvX+18BsM3sXeIGP1yt4\nn8gv/q7AV9z9kJk9TOTQ1tsWGWa1Gph+vA9x90oz+xaR54wY8Ly7z46xzsHgJP39ZnY/cITIoaOb\ngOiry54hchitnMhhtNVBexHwYzNrCNb9atD+EPCCmW1x94vM7F+AJ4PzNxA5B7KaY2UCv7PIY08N\neKCZx51KO6RRdUVEJG46bCUiInFTeIiISNwUHiIiEjeFh4iIxE3hISIicVN4iIhI3BQeIiISN4WH\niIjE7f8Dt9xXXVgBETcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25c4df45208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "K = [2, 3, 4, 5, 6]\n",
    "SSe = [3365.6772, 1515.8141, 943.30, 771.155, 392.64]\n",
    "plt.plot(K,SSe)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('SSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow]",
   "language": "python",
   "name": "conda-env-tensorflow-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
